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arXiv 2024年

作者： Hu, Xiao Chen, Ziqi Peng, Bo Adu-Ampratwum, Daniel Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Division of Medicinal Chemistry and Pharmacognosy College of Pharmacy The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States

Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. A key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM also implements a local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions. Through this hierarchical process, log-RRIM effectively captures how different molecular fragments contribute to and influence the overall reaction yield. log-RRIM shows superior performance in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. The framework’s sophisticated modeling of reactant-reagent interactions and precise capture of molecular fragment contributions make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://***/ninglab/Yield_log_RRIM. © 2024, CC BY.

关键词： Reagents

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Topological Representation Based on Wavelet Transform as a Novel Imaging Biomarker for Precise Histology Diagnosis of Tumors in Ultrasound Images: A Comprehensive Study

SSRN

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SSRN 2024年

作者： Wei, Qu Zeng, Si Jiang, Zekun Xu, Fashu Qin, Ziyuan Li, Kang Department of Ultrasound West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Med-X Center for Informatics Sichuan University Chengdu610041 China College of Computer Science Sichuan University Chengdu610041 China Case Western Reserve University Cleveland United States Shanghai Artificial Intelligence Laboratory Shanghai200240 China

Topological data analysis (TDA) and topological representation are emerging research directions in medical image analysis, aimed at mining the spatial topological features of diseases such as tumors, exploring biological complex patterns, and constructing predictive tools for clinical diagnosis and treatment. Ultrasound examinations are widely used in the preliminary diagnosis of various tumor diseases due to their convenience, real-time imaging, and non-radiation. However, since ultrasound examinations rely heavily on the subjective experience of doctors and often require pathological gold standards for tumor typing determination, there is an urgent need to develop novel quantitative analysis methods to build the correlation between ultrasound quantitative features and histological information, thereby optimizing clinical decision-making paths. The extensive evaluation and validation studies of TDA in ultrasound imaging are still scarcely reported. Here, we proposed a novel ultrasound topological representation method called WT-TDA, which is based on wavelet transformation to enhance topological feature representation and combines SHAP-based feature selection to optimize modeling effects. We conducted an extensive evaluation of WT-TDA on three ultrasound datasets, achieving ideal prediction results, with the best performance on dataset 1 reaching an accuracy of 0.944 and an AUC of 0.91. The study verifies the persistent homogeneity of ultrasound images and demonstrates the potential application of our method in the histology diagnosis of ultrasound tumors, which can help optimize ultrasound diagnosis and provide decision support for ultrasound doctors. © 2024, The Authors. All rights reserved.

关键词： Tumors

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Can AI Find the Needle in a Haystack?: The Ongoing Search for Undiagnosed Cardiac Amyloidosis

JACC: Advances

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JACC: Advances 2024年第9P2期3卷 100999-100999页

作者： Poterucha, Timothy J. Haggerty, Christopher M. Elias, Pierre Seymour Paul and Gloria Milstein Division of Cardiology Department of Medicine Columbia University Irving Medical Center New York NY United States Department of Biomedical Informatics Columbia University New York NY United States Information Technology Data Science NewYork-Presbyterian Hospital New York NY United States

Corresponding Author

关键词： amyloidosis artificial intelligence cardiac amyloidosis

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ARDS Subphenotypes Exhibit Different Right Ventricular-Pulmonary Arterial Coupling Profiles

CHEST Critical Care

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CHEST Critical Care 2025年第1期3卷 100119-100119页

作者： Siuba, Matthew T. Hockstein, Maxwell A. Rey, Diego Ariel Duggal, Abhijit Deliberato, Rodrigo Octavio Department of Critical Care Medicine Integrated Hospital Care Institute Cleveland Clinic Cleveland OH Department of Biostatistics Health Informatics and Data Science University of Cincinnati College of Medicine Cincinnati OH Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH Departments of Emergency Medicine and Critical Care MedStar Washington Hospital Center Washington DC Research Department Endpoint Health Inc. Palo Alto CA

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Predicting the physiological effects of multiple drugs using electronic health record

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Computers in Biology and Medicine 2025年 184卷 109485-109485页

作者： Jeon, Junhyeok Hong, Eujin Kim, Jong-Yeup Lee, Suehyun Kim, Hyun Uk Daejeon34141 Korea Republic of Department of Biomedical Informatics College of Medicine Konyang University Daejeon35365 Korea Republic of Healthcare Data Science Center Konyang University Hospital Daejeon35365 Korea Republic of Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Graduate School of Engineering Biology KAIST Daejeon34141 Korea Republic of BioProcess Engineering Research Center and BioInformatics Research Center KAIST Daejeon34141 Korea Republic of

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consider the patient information. To address this challenge, we use publicly available electronic health record (EHR), MIMIC-IV, to develop machine learning models that predict the physiological effects of two or more drugs. This study involves extensive preprocessing of laboratory measurement data, prescription data and patient data. The resulting machine learning models predict potential abnormalities across 20 selected measurement items (e.g., concentrations of metabolites and blood cells) in the form of a sentence. Analysis of the model predictions showed that age, specific active pharmaceutical ingredients, and male/female appeared to be the most influential features. The model development process showcased in this study can be extended to other measurement items for a target EHR. © 2024 Elsevier Ltd

关键词： Electronic health record

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

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IEEE International Symposium on biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

arXiv

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arXiv 2024年

作者： Takayama, Masayuki Okuda, Tadahisa Pham, Thong Ikenoue, Tatsuyoshi Fukuma, Shingo Shimizu, Shohei Sannai, Akiyoshi Data Science and AI Innovation Research Promotion Center Shiga University Japan Department of Health Data Science Tokyo Medical University Graduate School of Medicine Human Health Sciences Kyoto University Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Japan Graduate School of Medicine Human Health Sciences Kyoto University Department of Epidemiology Infectious Disease Control and Prevention Hiroshima University Graduate school of Biomedical and Health Sciences Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Institute for the Advanced Study of Human Biology Kyoto University Center for Advanced Intelligence Project RIKEN Japan Department of Physics Kyoto University Data Science and AI Innovation Research Promotion Center Shiga University Graduate School of Engineering The University of Tokyo Center for Advanced Intelligence Project RIKEN Research Development Center for Large Language Models National Institute of Informatics Japan

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, by using an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. © 2024, CC BY.

关键词： Computational linguistics

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Leveraging Error-Prone Algorithm-Derived Phenotypes: Enhancing Association Studies for Risk Factors in Ehr data

SSRN

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SSRN 2024年

作者： Lu, Yiwen Tong, Jiayi Chubak, Jessica Lumley, Thomas Hubbard, Rebecca A. Xu, Hua Chen, Yong Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States The Graduate Group in Applied Mathematics and Computational Science School of Arts and Sciences University of Pennsylvania PhiladelphiaPA United States Kaiser Permanente Washington Health Research Institute SeattleWA United States Department of Statistics Faculty of Science University of Auckland Auckland New Zealand Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States PhiladelphiaPA United States Department of Biomedical Informatics and Data Science Yale University New HavenCT United States Leonard Davis Institute of Health Economics PhiladelphiaPA United States PhiladelphiaPA United States

ObjectivesIt has become increasingly common for multiple computable phenotypes from electronic health records (EHR) to be developed for a given phenotype. However, EHR-based association studies often focus on a single phenotype. In this paper, we develop a method aiming to simultaneously make use of multiple EHR-derived phenotypes for reduction of bias due to phenotyping error and improved efficiency of phenotype/exposure *** and MethodsThe proposed method combines multiple algorithm-derived phenotypes with a small set of validated outcomes to reduce bias and improve estimation accuracy and efficiency. The performance of our method was evaluated through simulation studies and real-world application to an analysis of colon cancer recurrence using EHR data from Kaiser Permanente *** settings where there was no single surrogate performing uniformly better than all others in terms of both sensitivity and specificity, our method achieved substantial bias reduction compared to using a single algorithm-derived phenotype. Our method also led to higher estimation efficiency by up to 30% compared to an estimator that used only one algorithm-derived *** studies and application to real-world data demonstrated the effectiveness of our method in integrating multiple phenotypes, thereby enhancing bias reduction, statistical accuracy and *** method combines information across multiple surrogates using a statistically efficient seemingly unrelated regression framework. Our method provides a robust alternative to single-surrogate-based bias correction, especially in contexts lacking information on which surrogate is superior. © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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De novo reconstruction of satellite repeat units from sequence data

arXiv

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arXiv 2023年

作者： Zhang, Yujie Chu, Justin Cheng, Haoyu Li, Heng Harvard School of Public Health 677 Huntington Avenue BostonMA02115 United States Department of Data Science Dana-Farber Cancer Institute 450 Brookline Ave BostonMA02215 United States Department of Biomedical Informatics Harvard Medical School 10 Shattuck St BostonMA02115 United States

Satellite DNA are long tandemly repeating sequences in a genome and may be organized as high-order repeats (HORs). They are enriched in centromeres and are challenging to assemble. Existing algorithms for identifying satellite repeats either require the complete assembly of satellites or only work for simple repeat structures without HORs. Here we describe Satellite Repeat Finder (SRF), a new algorithm for reconstructing satellite repeat units and HORs from accurate reads or assemblies without prior knowledge on repeat structures. Applying SRF to real sequence data, we showed that SRF could reconstruct known satellites in human and well-studied model organisms. We also found satellite repeats are pervasive in various other species, accounting for up to 12% of their genome contents but are often underrepresented in assemblies. With the rapid progress on genome sequencing, SRF will help the annotation of new genomes and the study of satellite DNA evolution even if such repeats are not fully assembled. © 2023, CC BY.

关键词： Satellites

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